Abstract
It remains a challenge to obtain an accurate multi-step-ahead forecast of reservoir water availability for island areas. A novel hybrid multi-step-ahead forecast model is introduced to solve this problem. Partial autocorrelation function (PACF) is first used to analyze the characteristics of the target time series for extracting the appropriate input variables. Least-square support vector machine (LSSVM) with recursive mechanism is then explored for modelling multi-step-ahead forecasts, in which the forecast model is adjusted adaptively as long as new information is updated. The parameters in LSSVM are optimized based on an improved quantum-inspired version of Grey Wolf Optimizer (QGWO). The QGWO fortifies against the stagnation of the wolves at an optimal local point using three strategies including the quantum operator, non-linear convergence factor, and dynamic weighting. In this paper, the performance of QGWO was first demonstrated with that of other meta-heuristic (MH) algorithms in solving eight mathematical benchmark problems. Two time series of reservoir water availability in Zhoushan Islands, China, were then provided and analyzed to validate the performance of the advanced forecast approach in multi-step-ahead forecasts. The forecasts were compared with those obtained by the non-adaptive (NA) and non-recursive (NR) models. Results indicate that (1) QGWO displays superior performance on model parameters optimization than other comparative MH algorithms; (2) The proposed hybrid model in consideration of the nearest anterior feedback, as well as the adaptive mechanism, not only outperforms the two comparative models (NA, NR) but significantly enhances the accuracy of multi-step-ahead forecasts for non-stationary time series and low or high volume events, even as the forecast time horizon increases.
Published Version
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